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Does the Corona Borealis Supercluster Form a Giant Binary-Like System?

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Does the Corona Borealis Supercluster Form a Giant Binary-Like System? DOES THE CORONA BOREALIS SUPERCLUSTER FORM A GIANT BINARY-LIKE SYSTEM? Giovanni C. Baiesi Pillastrini1* ABSTRACT The distribution of local gravitational potentials generated by a complete volume-limited sample of galaxy groups and clusters filling the Corona Borealis region has been derived to search for new gravitational hints in the context of clus- tering analysis unrevealed by alternative methodologies. Mapping such a distribution as a function of spatial positions, the deepest potential wells in the sample trace unambiguously the locations of the densest galaxy cluster clumps provid- ing the physical keys to bring out gravitational features connected to the formation, composition and evolution of the major clustered structures filling that region. As expected, the three deepest potential wells found at Equatorial coordi- nates: (~ 230°, ~ 28°, z ~ 0.075), (~ 240°, ~ 27°, z ~ 0.09) and, (227°, 5.8°, z ~ 0.0788) correspond to massive super- clusters of galaxy groups and clusters identified as the Corona Borealis, A2142 and Virgo-Serpent, respectively. How- ever, the deepest isopotential contours around the Corona Borealis and A2142 superclusters seem to suggest a gravita- tional feature similar to a giant binary-like system connected by a filamentary structure. To a first approximation, it seems unlikely that this hypothesized system could be gravitationally bound. Keywords: methods: data analysis - galaxies: clusters: individual: Corona Borealis Supercluster, A2142 supercluster, Virgo-Serpent supercluster – Cosmology: large scale structures of the Universe 1 U.A.I. c/o Osservatorio Astronomico Fuligni - Via Lazio 14, 00040 Rocca di Papa (RM), Italy * permanent address: via Pizzardi, 13 - 40138 Bologna - Italy - email: [email protected] 1 1. INTRODUCTION 1.1. The Corona Borealis region (CBr) Studying the distribution and dynamics of galaxy superclusters in the local Universe, Bahcall and Soneira (1984) and, more recently, Luparello et al. (2011) analyzing the Corona Borealis region (CBr hereafter) hypothesized that the well- known Corona Borealis Supercluster (CBSCL hereafter) is part of a much more extended and massive structure. Stimu- lated in disentangling this issue, we attempt an exploratory analysis of that region based on the gravitational potential method (GPM hereafter; Baiesi Pillastrini 2013) with the main aim to search new gravitational hints and features unre- vealed by previous studies as well as to compare the efficiency of the GPM in identifying and quantifying clustered structured with the results of previous well-known studies. Since the first identification of the CBSCL by Abell (1961) using his own Catalog of Galaxy Clusters (Abell 1958), that region has been largely investigated using a variety of clus- tering algorithms generally based on the density field and Friend of Friend (FoF) analyses (Bahcall and Soneira 1984; Cappi and Maurogordato 1992; Zucca et al. 1993; Kalinkov and Kuneva 1995 Einasto et al. 1994, 1997, 2001, 2011a) and compared with the Abell cluster Catalog. On the other hand, many other dedicated studies have analyzed its com- position, morphology and dynamical state (Postman et al. 1988; Small et al. 1997, 1998; Kopylova and Kopylov 1998; Marini et al. 2004; Génova-Santos et al. 2010; Batiste and Batuski 2013; Pearson et al. 2014; Einasto et al. 2015; Gramann et al. 2015; Pearson 2015). A new generation of Supercluster catalogs constructed with accurate and complete datasets combined with new methodologies of the clustering analysis has provided insight on the extension and mem- bership of the CBSCL ( Einasto et al. 2006; Luparello et al. 2011; Liivamagi et al. 2012; Chow-Martinez et al. 2014). 1.2. Clustering algorithms vs. GPM The common practice of introducing selection parameters depending on well-motivated assumptions in the clustering algorithms and analyses such as linking lengths, spatial density thresholds, etc., often provides quite different boundary and membership to a certain structure. For example, the Abell clusters assignment to the CBSCL was subject to many revisions after the first definition of Abell (1961). In the present study, the GPM clustering algorithm based on the Newtonian gravity theory has been applied in order to detect the major clustered structures in the Corona Borealis re- gion, their main physical properties and, if any, unknown gravitational features. The GPM was developed following the prescription of the exploratory data analysis and rests on the basic idea that the gravitational potential is closely con- nected with the matter density field and that galaxy systems aggregate by following the laws of gravity no matter how different they are. As established by the theory of gravitational instability, the formation (and evolution) of huge scale structures seen in the galaxy distribution is tightly related to the potential field distribution (Madsen et al.1998). It fol- lows that clustered regions arise due to slow matter flows into negative potential wells so that, the detection of huge mass concentrations can be carried out simply observing the regions where the deepest potential wells (DPW hereafter) originate. Its application is becoming now possible after that accurate mass estimations become available in large gal- axy group/cluster catalogs up to intermediate redshift (see for instance Tempel et al. 2014). The use of large datasets of galaxy systems taken as mass tracers of gravitational potential wells is the most relevant difference between the GPM and alternative methods based on the analysis of space density or velocity fields. The GPM was designed to construct analytically a list of the deepest potential magnitudes of a complete volume-limited dataset of astronomical objects and, graphically, to display isopotential contours from which one can explore and identify the location of a single or more clustered structures simply looking for the deepest negative potential counterparts. Specifically, the GPM performs a two-step analysis as follows: after the identification in position and in magnitude of the DPWs, each DPW is assumed as the temporary center of mass then, by modeling an appropriate mass-radius relation, the quantitative parameters de- fining the mass overdensity can be iteratively computed until the final position of the center of mass remain constant. -1 -1 In the present study we assume: H0 = 100 h km s Mpc , Ωm = .27 and ΩΛ = .73 according to the cosmological parame- ters of the dataset used hereafter. The paper is organized as follows: in Sect.2 we briefly describe the GPM. In Sect.3 the GPM is applied to a complete volume-limited sample of galaxy groups and clusters filling the CBr with the purpose to identify the locations of the DPWs. Then, in Sect.4, the assumed criterion to quantify the mass distribution underlying the DPW clumps is described and applied. In Sect.5 the results are then compared with other studies. In Sect.6, the gravitational binding of the pro- posed binary system is tested. In Sect.7, conclusions are drawn. 2. A BRIEF DESCRIPTION OF THE GRAVITATIONAL POTENTIAL METHOD (GPM) 2.1. The algorithm design in the framework of the ΛCDM cosmological model The methodology of investigation adopted for the GPM is essentially based on the exploratory data analysis (Tukey 1977) in the framework of Newtonian mechanics with the aim to construct the local gravitational potential distribution generated by a complete volume-limited sample of astronomical objects. Now, being gravity a superposable force, the gravitational potential generated by a collection of point masses at a certain location in space is the sum of the potentials 2 generated at that location by each point mass taken in isolation. By measuring the local potential at the position of each object taken one at a time as a test-particle, the map of the local potential distribution generated by the spatial distribu- tion of the whole sample is displayed. The DPWs identify unambiguously the location of the densest clumps in a mass distribution. Now, in the framework of the ΛCDM cosmological model, the total potential acting on a test-particle is given by U g U where U g is the attractive component of the potential due to gravity and U is the repul- sive component of the potential due to dark energy. Given N point-masses located at position vectors d (from the V j i observer) within a spherical volume V j of fixed radius RV centered on a generic test-particle j at position vector d j from the observer then, potential components generated at position vector by the point masses mi (i 1,...., N ) are given by V j N 1 N 2 V j 4 V j = and = G mi di d j U G di d j i1,i j,iV j 3 i1,i j,iV j -30 3 where G is the gravitational constant and is dark energy density of ~6 x 10 g/cm (Plank collaboration, 2015). Repeating the calculation for each point-mass taken one at a time as a test-particle, we construct the whole j distri- bution. are given in 106 h(km/s)2 unit and is always ≤ 0. Since the GPM is a gravity-based method to detect gravitational clustering, for each point-mass j where the inequality U > U g is satisfied, is assumed = 0. This assumption is required to prevent objects dominated by the repulsive potential component to mixed up opposite actions as gravitational attraction and dark energy repulsion. An object dom- inated by the repulsive potential component must follow the accelerated expansion of the Universe, so that it cannot be taken into account in the clustering analysis to define bound structures. These objects will represent the zero-level of the distribution so that only features subject to gravitational attraction will be highlighted removing fake images and enhancing high-resolved images of real clustered structures. 2.2. Advantages and disadvantages The GPM provides several relevant advantage: i) it enables the identification of clustered structures using an algorithm based on gravity theory; ii) being gravity a long range force, the potential distribution is smoother than the density dis- tribution since the contribution to local potential fields due to small density fluctuations is irrelevant e.g.
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